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Udemy - Signal processing problems, solved in MATLAB and in Python

  • 收录时间:2020-01-06 18:18:50
  • 文件大小:6GB
  • 下载次数:81
  • 最近下载:2021-01-23 09:24:18
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文件列表

  1. 3. Spectral and rhythmicity analyses/3. Fourier transform for spectral analyses.mp4 174MB
  2. 10. Feature detection/6. Application Detect muscle movements from EMG recordings.mp4 151MB
  3. 7. Wavelet analysis/8. MATLAB Time-frequency analysis with complex wavelets.mp4 140MB
  4. 7. Wavelet analysis/5. Wavelet convolution for narrowband filtering.mp4 136MB
  5. 10. Feature detection/4. Wavelet convolution for feature extraction.mp4 136MB
  6. 11. Variability/3. Signal-to-noise ratio (SNR).mp4 133MB
  7. 10. Feature detection/7. Full width at half-maximum.mp4 131MB
  8. 10. Feature detection/2. Local maxima and minima.mp4 127MB
  9. 8. Resampling, interpolating, extrapolating/9. Dynamic time warping.mp4 123MB
  10. 3. Spectral and rhythmicity analyses/4. Welch's method and windowing.mp4 122MB
  11. 5. Filtering/3. FIR filters with firls.mp4 120MB
  12. 3. Spectral and rhythmicity analyses/2. Crash course on the Fourier transform.mp4 117MB
  13. 5. Filtering/2. Filtering Intuition, goals, and types.mp4 115MB
  14. 11. Variability/5. Entropy.mp4 112MB
  15. 8. Resampling, interpolating, extrapolating/3. Downsampling.mp4 111MB
  16. 2. Time series denoising/8. Remove nonlinear trend with polynomials.mp4 109MB
  17. 10. Feature detection/3. Recover signal from noise amplitude.mp4 104MB
  18. 8. Resampling, interpolating, extrapolating/2. Upsampling.mp4 101MB
  19. 6. Convolution/3. Convolution in MATLAB.mp4 101MB
  20. 5. Filtering/7. Avoid edge effects with reflection.mp4 99MB
  21. 2. Time series denoising/3. Gaussian-smooth a time series.mp4 96MB
  22. 8. Resampling, interpolating, extrapolating/6. Resample irregularly sampled data.mp4 94MB
  23. 7. Wavelet analysis/2. What are wavelets.mp4 93MB
  24. 10. Feature detection/5. Area under the curve.mp4 91MB
  25. 5. Filtering/15. Remove electrical line noise and its harmonics.mp4 91MB
  26. 5. Filtering/10. Windowed-sinc filters.mp4 88MB
  27. 6. Convolution/6. Thinking about convolution as spectral multiplication.mp4 88MB
  28. 5. Filtering/14. Quantifying roll-off characteristics.mp4 87MB
  29. 2. Time series denoising/10. Remove artifact via least-squares template-matching.mp4 85MB
  30. 5. Filtering/6. Causal and zero-phase-shift filters.mp4 82MB
  31. 5. Filtering/5. IIR Butterworth filters.mp4 80MB
  32. 9. Outlier detection/3. Outliers via local threshold exceedance.mp4 77MB
  33. 8. Resampling, interpolating, extrapolating/8. Spectral interpolation.mp4 77MB
  34. 2. Time series denoising/6. Median filter to remove spike noise.mp4 77MB
  35. 3. Spectral and rhythmicity analyses/5. Spectrogram of birdsong.mp4 76MB
  36. 11. Variability/2. Total and windowed variance and RMS.mp4 76MB
  37. 5. Filtering/16. Use filtering to separate birds in a recording.mp4 75MB
  38. 6. Convolution/2. Time-domain convolution.mp4 71MB
  39. 9. Outlier detection/2. Outliers via standard deviation threshold.mp4 70MB
  40. 6. Convolution/5. The convolution theorem.mp4 69MB
  41. 2. Time series denoising/2. Mean-smooth a time series.mp4 66MB
  42. 5. Filtering/8. Data length and filter kernel length.mp4 65MB
  43. 5. Filtering/9. Low-pass filters.mp4 64MB
  44. 7. Wavelet analysis/9. Time-frequency analysis of brain signals.mp4 63MB
  45. 2. Time series denoising/5. Denoising EMG signals via TKEO.mp4 57MB
  46. 5. Filtering/12. Narrow-band filters.mp4 56MB
  47. 4. Working with complex numbers/2. From the number line to the complex number plane.mp4 55MB
  48. 8. Resampling, interpolating, extrapolating/5. Interpolation.mp4 55MB
  49. 1. Introductions/5. Writing code vs. using toolboxesprograms.mp4 53MB
  50. 5. Filtering/11. High-pass filters.mp4 52MB
  51. 6. Convolution/8. Convolution with frequency-domain Gaussian (narrowband filter).mp4 52MB
  52. 2. Time series denoising/9. Averaging multiple repetitions (time-synchronous averaging).mp4 50MB
  53. 6. Convolution/7. Convolution with time-domain Gaussian (smoothing filter).mp4 49MB
  54. 7. Wavelet analysis/6. Overview Time-frequency analysis with complex wavelets.mp4 49MB
  55. 4. Working with complex numbers/7. Magnitude and phase of complex numbers.mp4 48MB
  56. 7. Wavelet analysis/3. Convolution with wavelets.mp4 48MB
  57. 5. Filtering/4. FIR filters with fir1.mp4 47MB
  58. 9. Outlier detection/4. Outlier time windows via sliding RMS.mp4 46MB
  59. 6. Convolution/9. Convolution with frequency-domain Planck taper (bandpass filter).mp4 46MB
  60. 8. Resampling, interpolating, extrapolating/4. Strategies for multirate signals.mp4 44MB
  61. 5. Filtering/13. Two-stage wide-band filter.mp4 42MB
  62. 2. Time series denoising/4. Gaussian-smooth a spike time series.mp4 42MB
  63. 9. Outlier detection/5. Code challenge.mp4 39MB
  64. 4. Working with complex numbers/4. Multiplication with complex numbers.mp4 39MB
  65. 8. Resampling, interpolating, extrapolating/7. Extrapolation.mp4 37MB
  66. 1. Introductions/3. Using Octave-online in this course.mp4 34MB
  67. 1. Introductions/1. Signal processing = decision-making + tools.mp4 33MB
  68. 11. Variability/4. Coefficient of variation (CV).mp4 29MB
  69. 1. Introductions/6. Using the Q&A forum.mp4 27MB
  70. 8. Resampling, interpolating, extrapolating/10. Code challenge denoise and downsample this signal!.mp4 25MB
  71. 1. Introductions/2. Using MATLAB in this course.mp4 24MB
  72. 10. Feature detection/8. Code challenge find the features!.mp4 24MB
  73. 1. Introductions/4. Using Python in this course.mp4 24MB
  74. 11. Variability/6. Code challenge.mp4 24MB
  75. 4. Working with complex numbers/5. The complex conjugate.mp4 23MB
  76. 6. Convolution/4. Why is the kernel flipped backwards!!!.mp4 23MB
  77. 11. Variability/1.1 sigprocMXC_variability.zip.zip 22MB
  78. 4. Working with complex numbers/3. Addition and subtraction with complex numbers.mp4 20MB
  79. 4. Working with complex numbers/6. Division with complex numbers.mp4 19MB
  80. 6. Convolution/6.1 TFtheory.mp4.mp4 18MB
  81. 6. Convolution/10. Code challenge Create a frequency-domain mean-smoothing filter.mp4 17MB
  82. 3. Spectral and rhythmicity analyses/6. Code challenge Compute a spectrogram!.mp4 15MB
  83. 7. Wavelet analysis/10. Code challenge Compare wavelet convolution and FIR filter!.mp4 13MB
  84. 2. Time series denoising/7. Remove linear trend (detrending).mp4 13MB
  85. 2. Time series denoising/1.1 sigprocMXC_TimeSeriesDenoising.zip.zip 12MB
  86. 5. Filtering/17. Code challenge Filter these signals!.mp4 11MB
  87. 2. Time series denoising/11. Code challenge Denoise these signals!.mp4 8MB
  88. 5. Filtering/1.1 sigprocMXC_filtering.zip.zip 5MB
  89. 3. Spectral and rhythmicity analyses/1.1 sigprocMXC_spectral.zip.zip 2MB
  90. 10. Feature detection/1.1 sigprocMXC_featuredet.zip.zip 2MB
  91. 7. Wavelet analysis/1.1 sigprocMXC_wavelets.zip.zip 770KB
  92. 8. Resampling, interpolating, extrapolating/1.1 sigprocMXC_resampling.zip.zip 411KB
  93. 9. Outlier detection/1.1 sigprocMXC_outliers.zip.zip 268KB
  94. 6. Convolution/1.1 sigprocMXC_convolution.zip.zip 250KB
  95. 4. Working with complex numbers/1.1 sigprocMXC_complex.zip.zip 38KB
  96. 3. Spectral and rhythmicity analyses/3. Fourier transform for spectral analyses.vtt 23KB
  97. 10. Feature detection/7. Full width at half-maximum.vtt 21KB
  98. 10. Feature detection/6. Application Detect muscle movements from EMG recordings.vtt 21KB
  99. 11. Variability/5. Entropy.vtt 20KB
  100. 8. Resampling, interpolating, extrapolating/9. Dynamic time warping.vtt 20KB
  101. 5. Filtering/2. Filtering Intuition, goals, and types.vtt 19KB
  102. 10. Feature detection/2. Local maxima and minima.vtt 19KB
  103. 3. Spectral and rhythmicity analyses/2. Crash course on the Fourier transform.vtt 19KB
  104. 3. Spectral and rhythmicity analyses/4. Welch's method and windowing.vtt 18KB
  105. 2. Time series denoising/8. Remove nonlinear trend with polynomials.vtt 18KB
  106. 11. Variability/3. Signal-to-noise ratio (SNR).vtt 18KB
  107. 7. Wavelet analysis/8. MATLAB Time-frequency analysis with complex wavelets.vtt 18KB
  108. 5. Filtering/3. FIR filters with firls.vtt 18KB
  109. 7. Wavelet analysis/2. What are wavelets.vtt 17KB
  110. 7. Wavelet analysis/5. Wavelet convolution for narrowband filtering.vtt 17KB
  111. 10. Feature detection/4. Wavelet convolution for feature extraction.vtt 17KB
  112. 2. Time series denoising/3. Gaussian-smooth a time series.vtt 16KB
  113. 8. Resampling, interpolating, extrapolating/2. Upsampling.vtt 16KB
  114. 6. Convolution/3. Convolution in MATLAB.vtt 16KB
  115. 10. Feature detection/5. Area under the curve.vtt 15KB
  116. 6. Convolution/6. Thinking about convolution as spectral multiplication.vtt 15KB
  117. 8. Resampling, interpolating, extrapolating/3. Downsampling.vtt 15KB
  118. 6. Convolution/2. Time-domain convolution.vtt 15KB
  119. 10. Feature detection/3. Recover signal from noise amplitude.vtt 15KB
  120. 5. Filtering/10. Windowed-sinc filters.vtt 14KB
  121. 5. Filtering/7. Avoid edge effects with reflection.vtt 14KB
  122. 5. Filtering/14. Quantifying roll-off characteristics.vtt 13KB
  123. 8. Resampling, interpolating, extrapolating/6. Resample irregularly sampled data.vtt 13KB
  124. 11. Variability/2. Total and windowed variance and RMS.vtt 13KB
  125. 8. Resampling, interpolating, extrapolating/8. Spectral interpolation.vtt 12KB
  126. 4. Working with complex numbers/2. From the number line to the complex number plane.vtt 12KB
  127. 5. Filtering/5. IIR Butterworth filters.vtt 12KB
  128. 2. Time series denoising/10. Remove artifact via least-squares template-matching.vtt 12KB
  129. 2. Time series denoising/6. Median filter to remove spike noise.vtt 12KB
  130. 5. Filtering/15. Remove electrical line noise and its harmonics.vtt 12KB
  131. 6. Convolution/5. The convolution theorem.vtt 12KB
  132. 5. Filtering/6. Causal and zero-phase-shift filters.vtt 12KB
  133. 9. Outlier detection/2. Outliers via standard deviation threshold.vtt 12KB
  134. 9. Outlier detection/3. Outliers via local threshold exceedance.vtt 11KB
  135. 2. Time series denoising/2. Mean-smooth a time series.vtt 10KB
  136. 7. Wavelet analysis/9. Time-frequency analysis of brain signals.vtt 10KB
  137. 5. Filtering/8. Data length and filter kernel length.vtt 10KB
  138. 2. Time series denoising/5. Denoising EMG signals via TKEO.vtt 10KB
  139. 3. Spectral and rhythmicity analyses/5. Spectrogram of birdsong.vtt 10KB
  140. 7. Wavelet analysis/6. Overview Time-frequency analysis with complex wavelets.vtt 10KB
  141. 8. Resampling, interpolating, extrapolating/5. Interpolation.vtt 9KB
  142. 4. Working with complex numbers/7. Magnitude and phase of complex numbers.vtt 9KB
  143. 5. Filtering/9. Low-pass filters.vtt 9KB
  144. 1. Introductions/5. Writing code vs. using toolboxesprograms.vtt 8KB
  145. 6. Convolution/8. Convolution with frequency-domain Gaussian (narrowband filter).vtt 8KB
  146. 4. Working with complex numbers/4. Multiplication with complex numbers.vtt 8KB
  147. 8. Resampling, interpolating, extrapolating/4. Strategies for multirate signals.vtt 8KB
  148. 5. Filtering/12. Narrow-band filters.vtt 8KB
  149. 5. Filtering/16. Use filtering to separate birds in a recording.vtt 8KB
  150. 6. Convolution/9. Convolution with frequency-domain Planck taper (bandpass filter).vtt 7KB
  151. 6. Convolution/7. Convolution with time-domain Gaussian (smoothing filter).vtt 7KB
  152. 5. Filtering/11. High-pass filters.vtt 7KB
  153. 8. Resampling, interpolating, extrapolating/7. Extrapolation.vtt 7KB
  154. 9. Outlier detection/4. Outlier time windows via sliding RMS.vtt 7KB
  155. 5. Filtering/4. FIR filters with fir1.vtt 7KB
  156. 7. Wavelet analysis/3. Convolution with wavelets.vtt 7KB
  157. 2. Time series denoising/9. Averaging multiple repetitions (time-synchronous averaging).vtt 6KB
  158. 2. Time series denoising/4. Gaussian-smooth a spike time series.vtt 6KB
  159. 1. Introductions/6. Using the Q&A forum.vtt 6KB
  160. 1. Introductions/3. Using Octave-online in this course.vtt 6KB
  161. 11. Variability/4. Coefficient of variation (CV).vtt 6KB
  162. 6. Convolution/4. Why is the kernel flipped backwards!!!.vtt 6KB
  163. 5. Filtering/13. Two-stage wide-band filter.vtt 5KB
  164. 4. Working with complex numbers/5. The complex conjugate.vtt 5KB
  165. 1. Introductions/1. Signal processing = decision-making + tools.vtt 5KB
  166. 8. Resampling, interpolating, extrapolating/10. Code challenge denoise and downsample this signal!.vtt 5KB
  167. 1. Introductions/2. Using MATLAB in this course.vtt 5KB
  168. 9. Outlier detection/5. Code challenge.vtt 5KB
  169. 4. Working with complex numbers/6. Division with complex numbers.vtt 4KB
  170. 4. Working with complex numbers/3. Addition and subtraction with complex numbers.vtt 4KB
  171. 1. Introductions/4. Using Python in this course.vtt 4KB
  172. 10. Feature detection/8. Code challenge find the features!.vtt 4KB
  173. 11. Variability/6. Code challenge.vtt 4KB
  174. 3. Spectral and rhythmicity analyses/6. Code challenge Compute a spectrogram!.vtt 3KB
  175. 2. Time series denoising/7. Remove linear trend (detrending).vtt 3KB
  176. 7. Wavelet analysis/10. Code challenge Compare wavelet convolution and FIR filter!.vtt 3KB
  177. 12. Discounts on related courses/2. Bonus Coupons for related courses.html 3KB
  178. 6. Convolution/10. Code challenge Create a frequency-domain mean-smoothing filter.vtt 2KB
  179. 5. Filtering/17. Code challenge Filter these signals!.vtt 2KB
  180. 2. Time series denoising/11. Code challenge Denoise these signals!.vtt 1KB
  181. 7. Wavelet analysis/7. Link to youtube channel with 3 hours of relevant material.html 621B
  182. 12. Discounts on related courses/1. Join the community!.html 553B
  183. 7. Wavelet analysis/4. Scientific publication about defining Morlet wavelets.html 465B
  184. Visit Getnewcourses.com.url 343B
  185. Visit Freecourseit.com.url 342B
  186. 1. Introductions/ReadMe.txt 241B
  187. ReadMe.txt 241B
  188. 3. Spectral and rhythmicity analyses/1. MATLAB and Python code for this section.html 99B
  189. 5. Filtering/1. MATLAB and Python code for this section.html 85B
  190. 2. Time series denoising/1. MATLAB and Python code for this section.html 84B
  191. 7. Wavelet analysis/1. MATLAB and Python code for this section.html 84B
  192. 10. Feature detection/1. MATLAB and Python code for this section.html 73B
  193. 6. Convolution/1. MATLAB and Python code for this section.html 72B
  194. 9. Outlier detection/1. MATLAB and Python code for this section.html 72B
  195. 8. Resampling, interpolating, extrapolating/1. MATLAB and Python code for this section.html 67B
  196. 11. Variability/1. MATLAB and Python code for this section.html 47B
  197. 4. Working with complex numbers/1. MATLAB and Python code for this section.html 46B